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The network output is slightly different from our expectations #39
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Hi @xa0082249956 , |
Hi, we are training by python scripts/train.py \
--dataset_type=ffhq_encode \
--exp_dir=experiment_disney \
--workers=8 \
--batch_size=2 \
--test_batch_size=2 \
--test_workers=8 \
--val_interval=2500 \
--save_interval=5000 \
--encoder_type=GradualStyleEncoder \
--start_from_latent_avg \
--lpips_lambda=0.8 \
--l2_lambda=1 \
--id_lambda=1 \
--w_norm_lambda=0.025 \
--stylegan_weights /data/pixel2style2pixel/ffhq_cartoon_blended.pt And the data are pairs like (real,toon). |
Cool. So a few points:
|
Great! Thanks for your reply. I will try it. |
Two questions regarding "we use about 50k pairs of pictures generated by Toonify Stylegan to do pairs-training".
|
Hi @brightmart To do paired training, you need to first generated pairs of (real, toon) images. While I don't know exactly how the paired data was generated in this case, you can check out the following Google Colab from Justin Pinkney to see an example of how to generate the paired images: |
Hi,
we use about 50k pairs of pictures generated by Toonify Stylegan to do pairs-training, and use the Toonify model provided in the
readme
as the weight.After training, the results in the log are all very good. However, other results are not as expected.
For example, this image is given by the latent code optimized by Stylegan.
However, pixel2style2pixel gives this one.
More training results from pixel2style2pixel:
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